Overview

Dataset statistics

Number of variables19
Number of observations351
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.2 KiB
Average record size in memory152.4 B

Variable types

Categorical1
Numeric18

Alerts

ANNUAL is highly overall correlated with AUG and 2 other fieldsHigh correlation
APR is highly overall correlated with MAMHigh correlation
AUG is highly overall correlated with ANNUAL and 1 other fieldsHigh correlation
FEB is highly overall correlated with JFHigh correlation
JAN is highly overall correlated with JFHigh correlation
JF is highly overall correlated with FEB and 1 other fieldsHigh correlation
JJAS is highly overall correlated with ANNUAL and 2 other fieldsHigh correlation
JUL is highly overall correlated with ANNUAL and 1 other fieldsHigh correlation
MAM is highly overall correlated with APR and 1 other fieldsHigh correlation
MAY is highly overall correlated with MAMHigh correlation
NOV is highly overall correlated with ONDHigh correlation
OCT is highly overall correlated with ONDHigh correlation
OND is highly overall correlated with NOV and 1 other fieldsHigh correlation
SUBDIVISION is uniformly distributedUniform
YEAR is uniformly distributedUniform
JAN has 57 (16.2%) zerosZeros
FEB has 64 (18.2%) zerosZeros
MAR has 46 (13.1%) zerosZeros
APR has 21 (6.0%) zerosZeros
MAY has 5 (1.4%) zerosZeros
OCT has 7 (2.0%) zerosZeros
NOV has 79 (22.5%) zerosZeros
DEC has 94 (26.8%) zerosZeros
JF has 18 (5.1%) zerosZeros

Reproduction

Analysis started2024-05-04 15:03:43.406597
Analysis finished2024-05-04 15:04:42.341243
Duration58.93 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

SUBDIVISION
Categorical

UNIFORM 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
West Madhya Pradesh
117 
East Madhya Pradesh
117 
Madhya Maharashtra
117 

Length

Max length19
Median length19
Mean length18.666667
Min length18

Characters and Unicode

Total characters6552
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest Madhya Pradesh
2nd rowWest Madhya Pradesh
3rd rowWest Madhya Pradesh
4th rowWest Madhya Pradesh
5th rowWest Madhya Pradesh

Common Values

ValueCountFrequency (%)
West Madhya Pradesh 117
33.3%
East Madhya Pradesh 117
33.3%
Madhya Maharashtra 117
33.3%

Length

2024-05-04T15:04:42.457608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T15:04:42.634475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
madhya 351
37.5%
pradesh 234
25.0%
west 117
 
12.5%
east 117
 
12.5%
maharashtra 117
 
12.5%

Most occurring characters

ValueCountFrequency (%)
a 1521
23.2%
h 819
12.5%
s 585
 
8.9%
585
 
8.9%
d 585
 
8.9%
M 468
 
7.1%
r 468
 
7.1%
e 351
 
5.4%
t 351
 
5.4%
y 351
 
5.4%
Other values (3) 468
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1521
23.2%
h 819
12.5%
s 585
 
8.9%
585
 
8.9%
d 585
 
8.9%
M 468
 
7.1%
r 468
 
7.1%
e 351
 
5.4%
t 351
 
5.4%
y 351
 
5.4%
Other values (3) 468
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1521
23.2%
h 819
12.5%
s 585
 
8.9%
585
 
8.9%
d 585
 
8.9%
M 468
 
7.1%
r 468
 
7.1%
e 351
 
5.4%
t 351
 
5.4%
y 351
 
5.4%
Other values (3) 468
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1521
23.2%
h 819
12.5%
s 585
 
8.9%
585
 
8.9%
d 585
 
8.9%
M 468
 
7.1%
r 468
 
7.1%
e 351
 
5.4%
t 351
 
5.4%
y 351
 
5.4%
Other values (3) 468
 
7.1%

YEAR
Real number (ℝ)

UNIFORM 

Distinct117
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1959
Minimum1901
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:42.801208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile1906.5
Q11930
median1959
Q31988
95-th percentile2011.5
Maximum2017
Range116
Interquartile range (IQR)58

Descriptive statistics

Standard deviation33.821971
Coefficient of variation (CV)0.017264916
Kurtosis-1.2001581
Mean1959
Median Absolute Deviation (MAD)29
Skewness0
Sum687609
Variance1143.9257
MonotonicityNot monotonic
2024-05-04T15:04:42.998842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1901 3
 
0.9%
1975 3
 
0.9%
1987 3
 
0.9%
1986 3
 
0.9%
1985 3
 
0.9%
1984 3
 
0.9%
1983 3
 
0.9%
1982 3
 
0.9%
1981 3
 
0.9%
1980 3
 
0.9%
Other values (107) 321
91.5%
ValueCountFrequency (%)
1901 3
0.9%
1902 3
0.9%
1903 3
0.9%
1904 3
0.9%
1905 3
0.9%
1906 3
0.9%
1907 3
0.9%
1908 3
0.9%
1909 3
0.9%
1910 3
0.9%
ValueCountFrequency (%)
2017 3
0.9%
2016 3
0.9%
2015 3
0.9%
2014 3
0.9%
2013 3
0.9%
2012 3
0.9%
2011 3
0.9%
2010 3
0.9%
2009 3
0.9%
2008 3
0.9%

JAN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct170
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.484046
Minimum0
Maximum120.7
Zeros57
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:43.194836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.35
median3.3
Q314.85
95-th percentile42
Maximum120.7
Range120.7
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation16.246411
Coefficient of variation (CV)1.5496318
Kurtosis12.353613
Mean10.484046
Median Absolute Deviation (MAD)3.3
Skewness2.9175963
Sum3679.9
Variance263.94586
MonotonicityNot monotonic
2024-05-04T15:04:43.414132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
16.2%
0.1 18
 
5.1%
0.2 10
 
2.8%
0.4 9
 
2.6%
2.1 6
 
1.7%
3.5 6
 
1.7%
2 5
 
1.4%
0.8 4
 
1.1%
1.2 4
 
1.1%
1 4
 
1.1%
Other values (160) 228
65.0%
ValueCountFrequency (%)
0 57
16.2%
0.1 18
 
5.1%
0.2 10
 
2.8%
0.3 3
 
0.9%
0.4 9
 
2.6%
0.5 1
 
0.3%
0.6 3
 
0.9%
0.7 4
 
1.1%
0.8 4
 
1.1%
0.9 2
 
0.6%
ValueCountFrequency (%)
120.7 1
0.3%
115.6 1
0.3%
86 1
0.3%
70.9 1
0.3%
66.1 1
0.3%
60.6 1
0.3%
54.1 1
0.3%
53.7 1
0.3%
52.3 1
0.3%
50.5 1
0.3%

FEB
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct150
Distinct (%)42.9%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean8.7122857
Minimum0
Maximum103.1
Zeros64
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:44.275211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median2.3
Q310.675
95-th percentile37.875
Maximum103.1
Range103.1
Interquartile range (IQR)10.475

Descriptive statistics

Standard deviation14.93711
Coefficient of variation (CV)1.714488
Kurtosis11.392998
Mean8.7122857
Median Absolute Deviation (MAD)2.3
Skewness2.9971495
Sum3049.3
Variance223.11724
MonotonicityNot monotonic
2024-05-04T15:04:44.485091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
18.2%
0.1 18
 
5.1%
0.2 15
 
4.3%
0.7 8
 
2.3%
0.4 8
 
2.3%
0.6 8
 
2.3%
0.8 7
 
2.0%
1.7 6
 
1.7%
0.5 5
 
1.4%
1.8 4
 
1.1%
Other values (140) 207
59.0%
ValueCountFrequency (%)
0 64
18.2%
0.1 18
 
5.1%
0.2 15
 
4.3%
0.3 4
 
1.1%
0.4 8
 
2.3%
0.5 5
 
1.4%
0.6 8
 
2.3%
0.7 8
 
2.3%
0.8 7
 
2.0%
0.9 4
 
1.1%
ValueCountFrequency (%)
103.1 1
0.3%
99 1
0.3%
79.8 1
0.3%
73.6 1
0.3%
68.4 1
0.3%
59.7 1
0.3%
59.6 1
0.3%
58.4 1
0.3%
52.8 1
0.3%
49.7 1
0.3%

MAR
Real number (ℝ)

ZEROS 

Distinct146
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4296296
Minimum0
Maximum87.3
Zeros46
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:44.680260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median2.2
Q38.6
95-th percentile34.2
Maximum87.3
Range87.3
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation12.546705
Coefficient of variation (CV)1.688739
Kurtosis10.163514
Mean7.4296296
Median Absolute Deviation (MAD)2.2
Skewness2.9294379
Sum2607.8
Variance157.41981
MonotonicityNot monotonic
2024-05-04T15:04:44.863931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
13.1%
0.1 23
 
6.6%
0.3 12
 
3.4%
0.2 11
 
3.1%
0.6 10
 
2.8%
0.5 8
 
2.3%
2.2 8
 
2.3%
1.5 8
 
2.3%
2.5 6
 
1.7%
0.4 6
 
1.7%
Other values (136) 213
60.7%
ValueCountFrequency (%)
0 46
13.1%
0.1 23
6.6%
0.2 11
 
3.1%
0.3 12
 
3.4%
0.4 6
 
1.7%
0.5 8
 
2.3%
0.6 10
 
2.8%
0.7 5
 
1.4%
0.8 3
 
0.9%
0.9 3
 
0.9%
ValueCountFrequency (%)
87.3 1
0.3%
73.4 1
0.3%
63.8 1
0.3%
56.1 1
0.3%
53.5 1
0.3%
53.3 1
0.3%
52.6 1
0.3%
50.3 1
0.3%
49.2 1
0.3%
48.2 1
0.3%

APR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct141
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1376068
Minimum0
Maximum72.4
Zeros21
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:45.045861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.9
median3.1
Q37.75
95-th percentile22.25
Maximum72.4
Range72.4
Interquartile range (IQR)6.85

Descriptive statistics

Standard deviation8.7764333
Coefficient of variation (CV)1.4299439
Kurtosis14.635203
Mean6.1376068
Median Absolute Deviation (MAD)2.7
Skewness3.2173073
Sum2154.3
Variance77.025782
MonotonicityNot monotonic
2024-05-04T15:04:45.230548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
6.0%
0.1 18
 
5.1%
0.2 16
 
4.6%
1 9
 
2.6%
1.7 8
 
2.3%
1.4 8
 
2.3%
0.4 7
 
2.0%
2.5 7
 
2.0%
3 6
 
1.7%
0.7 6
 
1.7%
Other values (131) 245
69.8%
ValueCountFrequency (%)
0 21
6.0%
0.1 18
5.1%
0.2 16
4.6%
0.3 4
 
1.1%
0.4 7
 
2.0%
0.5 4
 
1.1%
0.6 5
 
1.4%
0.7 6
 
1.7%
0.8 5
 
1.4%
0.9 3
 
0.9%
ValueCountFrequency (%)
72.4 1
0.3%
54.5 1
0.3%
47.7 1
0.3%
43.5 1
0.3%
41 1
0.3%
37.1 1
0.3%
36.6 1
0.3%
31.3 1
0.3%
31 1
0.3%
30.5 1
0.3%

MAY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct202
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.268946
Minimum0
Maximum104.2
Zeros5
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:45.468472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q12.5
median6.9
Q317.8
95-th percentile49.05
Maximum104.2
Range104.2
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation17.116319
Coefficient of variation (CV)1.2899532
Kurtosis6.5608409
Mean13.268946
Median Absolute Deviation (MAD)5.3
Skewness2.3792114
Sum4657.4
Variance292.96838
MonotonicityNot monotonic
2024-05-04T15:04:45.662063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 7
 
2.0%
0.9 6
 
1.7%
1.4 6
 
1.7%
1.3 6
 
1.7%
1.1 5
 
1.4%
7.7 5
 
1.4%
0.6 5
 
1.4%
2 5
 
1.4%
2.9 5
 
1.4%
0 5
 
1.4%
Other values (192) 296
84.3%
ValueCountFrequency (%)
0 5
1.4%
0.1 2
 
0.6%
0.2 4
1.1%
0.3 4
1.1%
0.4 3
0.9%
0.5 2
 
0.6%
0.6 5
1.4%
0.7 4
1.1%
0.8 4
1.1%
0.9 6
1.7%
ValueCountFrequency (%)
104.2 1
0.3%
91.2 1
0.3%
90.5 1
0.3%
81.5 1
0.3%
78.2 1
0.3%
77.2 1
0.3%
76.5 1
0.3%
74.2 1
0.3%
63.4 1
0.3%
63.2 1
0.3%

JUN
Real number (ℝ)

Distinct331
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.23732
Minimum12.1
Maximum356.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:45.859419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.1
5-th percentile39.6
Q184.1
median124.7
Q3177.95
95-th percentile256.15
Maximum356.6
Range344.5
Interquartile range (IQR)93.85

Descriptive statistics

Standard deviation67.08071
Coefficient of variation (CV)0.50346787
Kurtosis0.20008967
Mean133.23732
Median Absolute Deviation (MAD)46.3
Skewness0.67599046
Sum46766.3
Variance4499.8217
MonotonicityNot monotonic
2024-05-04T15:04:46.060490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.3 3
 
0.9%
254.4 2
 
0.6%
96.4 2
 
0.6%
124.7 2
 
0.6%
94.3 2
 
0.6%
34.4 2
 
0.6%
158.9 2
 
0.6%
91.4 2
 
0.6%
189.2 2
 
0.6%
163.9 2
 
0.6%
Other values (321) 330
94.0%
ValueCountFrequency (%)
12.1 1
0.3%
16.4 1
0.3%
21.9 1
0.3%
26.3 1
0.3%
27.2 1
0.3%
28 1
0.3%
30.6 1
0.3%
30.7 1
0.3%
31.1 1
0.3%
34.3 1
0.3%
ValueCountFrequency (%)
356.6 1
0.3%
346 1
0.3%
332.5 1
0.3%
315.2 1
0.3%
311.9 1
0.3%
307.6 1
0.3%
306.3 1
0.3%
300.7 1
0.3%
293.8 1
0.3%
286 1
0.3%

JUL
Real number (ℝ)

HIGH CORRELATION 

Distinct335
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.87863
Minimum46.9
Maximum624.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:46.333917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46.9
5-th percentile152.4
Q1231.15
median303.3
Q3369.7
95-th percentile516.4
Maximum624.4
Range577.5
Interquartile range (IQR)138.55

Descriptive statistics

Standard deviation106.65264
Coefficient of variation (CV)0.34528979
Kurtosis0.092160344
Mean308.87863
Median Absolute Deviation (MAD)68.7
Skewness0.45817231
Sum108416.4
Variance11374.785
MonotonicityNot monotonic
2024-05-04T15:04:46.662715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262.5 3
 
0.9%
318.7 3
 
0.9%
290 2
 
0.6%
238.9 2
 
0.6%
327.2 2
 
0.6%
361.2 2
 
0.6%
261.4 2
 
0.6%
411.9 2
 
0.6%
215.9 2
 
0.6%
376.3 2
 
0.6%
Other values (325) 329
93.7%
ValueCountFrequency (%)
46.9 1
0.3%
68.4 1
0.3%
84.7 1
0.3%
95.6 1
0.3%
97.7 1
0.3%
111.1 1
0.3%
111.7 1
0.3%
113.9 1
0.3%
120.6 1
0.3%
124.2 1
0.3%
ValueCountFrequency (%)
624.4 1
0.3%
605.6 1
0.3%
597.9 1
0.3%
597.2 1
0.3%
575 1
0.3%
561.6 1
0.3%
558 1
0.3%
553.4 1
0.3%
553.1 1
0.3%
552.2 1
0.3%

AUG
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.4849
Minimum59.5
Maximum713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:47.016137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum59.5
5-th percentile105
Q1189.4
median262
Q3362.65
95-th percentile489.95
Maximum713
Range653.5
Interquartile range (IQR)173.25

Descriptive statistics

Standard deviation119.38112
Coefficient of variation (CV)0.42562405
Kurtosis0.14186656
Mean280.4849
Median Absolute Deviation (MAD)82.9
Skewness0.5828106
Sum98450.2
Variance14251.852
MonotonicityNot monotonic
2024-05-04T15:04:47.390615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
369.5 2
 
0.6%
281.3 2
 
0.6%
358.9 2
 
0.6%
220.6 2
 
0.6%
415.2 2
 
0.6%
379.5 2
 
0.6%
270.2 2
 
0.6%
179.1 2
 
0.6%
480.8 2
 
0.6%
243.9 2
 
0.6%
Other values (324) 331
94.3%
ValueCountFrequency (%)
59.5 1
0.3%
63.6 1
0.3%
67.2 1
0.3%
73.7 1
0.3%
79 1
0.3%
79.4 1
0.3%
80 1
0.3%
80.9 1
0.3%
82.7 1
0.3%
85.4 2
0.6%
ValueCountFrequency (%)
713 1
0.3%
676.3 1
0.3%
590.3 1
0.3%
589.2 1
0.3%
588.6 1
0.3%
585.4 1
0.3%
568.5 1
0.3%
555.7 1
0.3%
551.4 1
0.3%
545.1 1
0.3%

SEP
Real number (ℝ)

Distinct328
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.51254
Minimum17.3
Maximum559.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:47.760308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17.3
5-th percentile50.8
Q1104.4
median162
Q3222.25
95-th percentile318.7
Maximum559.4
Range542.1
Interquartile range (IQR)117.85

Descriptive statistics

Standard deviation89.055757
Coefficient of variation (CV)0.52228276
Kurtosis2.2627136
Mean170.51254
Median Absolute Deviation (MAD)59.1
Skewness1.0710177
Sum59849.9
Variance7930.9278
MonotonicityNot monotonic
2024-05-04T15:04:48.004282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.6 3
 
0.9%
74.1 2
 
0.6%
162.6 2
 
0.6%
217 2
 
0.6%
63.1 2
 
0.6%
169.5 2
 
0.6%
88.8 2
 
0.6%
226 2
 
0.6%
242.8 2
 
0.6%
63.5 2
 
0.6%
Other values (318) 330
94.0%
ValueCountFrequency (%)
17.3 1
0.3%
20.1 1
0.3%
25.1 1
0.3%
27.5 1
0.3%
29.9 1
0.3%
31.6 1
0.3%
33.4 1
0.3%
34.7 1
0.3%
34.9 1
0.3%
35.8 1
0.3%
ValueCountFrequency (%)
559.4 1
0.3%
538.7 1
0.3%
522.7 1
0.3%
493.3 1
0.3%
480.1 1
0.3%
417 1
0.3%
415.9 1
0.3%
396.4 1
0.3%
395.1 1
0.3%
358.4 1
0.3%

OCT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct288
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.992593
Minimum0
Maximum196
Zeros7
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:48.193597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q18.2
median33.9
Q366.75
95-th percentile140.4
Maximum196
Range196
Interquartile range (IQR)58.55

Descriptive statistics

Standard deviation44.802421
Coefficient of variation (CV)0.97412253
Kurtosis0.82799586
Mean45.992593
Median Absolute Deviation (MAD)27.8
Skewness1.1694876
Sum16143.4
Variance2007.2569
MonotonicityNot monotonic
2024-05-04T15:04:48.395283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
2.0%
0.9 4
 
1.1%
1.2 4
 
1.1%
5.9 4
 
1.1%
1.4 3
 
0.9%
2.9 3
 
0.9%
0.6 3
 
0.9%
0.1 3
 
0.9%
0.2 3
 
0.9%
12.5 3
 
0.9%
Other values (278) 314
89.5%
ValueCountFrequency (%)
0 7
2.0%
0.1 3
0.9%
0.2 3
0.9%
0.3 2
 
0.6%
0.4 1
 
0.3%
0.5 2
 
0.6%
0.6 3
0.9%
0.7 1
 
0.3%
0.9 4
1.1%
1 1
 
0.3%
ValueCountFrequency (%)
196 1
0.3%
192.8 1
0.3%
180.9 1
0.3%
177.9 1
0.3%
177.6 1
0.3%
177.4 1
0.3%
171.3 1
0.3%
169.5 1
0.3%
162.5 1
0.3%
159 1
0.3%

NOV
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.727066
Minimum0
Maximum152.8
Zeros79
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:48.625345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.15
median3.8
Q321.95
95-th percentile78.45
Maximum152.8
Range152.8
Interquartile range (IQR)21.8

Descriptive statistics

Standard deviation27.910962
Coefficient of variation (CV)1.6686108
Kurtosis5.7170717
Mean16.727066
Median Absolute Deviation (MAD)3.8
Skewness2.3633435
Sum5871.2
Variance779.02181
MonotonicityNot monotonic
2024-05-04T15:04:48.820752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
22.5%
0.2 9
 
2.6%
0.3 9
 
2.6%
0.1 9
 
2.6%
0.4 5
 
1.4%
1.1 5
 
1.4%
1.2 4
 
1.1%
1.3 4
 
1.1%
2.2 4
 
1.1%
3.1 3
 
0.9%
Other values (172) 220
62.7%
ValueCountFrequency (%)
0 79
22.5%
0.1 9
 
2.6%
0.2 9
 
2.6%
0.3 9
 
2.6%
0.4 5
 
1.4%
0.5 2
 
0.6%
0.6 3
 
0.9%
0.7 2
 
0.6%
0.8 1
 
0.3%
0.9 1
 
0.3%
ValueCountFrequency (%)
152.8 1
0.3%
141.1 1
0.3%
137.1 1
0.3%
130.4 1
0.3%
121.3 1
0.3%
117.9 1
0.3%
112.1 1
0.3%
108.9 1
0.3%
104.1 1
0.3%
103.8 1
0.3%

DEC
Real number (ℝ)

ZEROS 

Distinct124
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7555556
Minimum0
Maximum102.6
Zeros94
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:49.031150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q36.2
95-th percentile33.8
Maximum102.6
Range102.6
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation13.891978
Coefficient of variation (CV)2.0563783
Kurtosis17.37607
Mean6.7555556
Median Absolute Deviation (MAD)1.5
Skewness3.7834371
Sum2371.2
Variance192.98705
MonotonicityNot monotonic
2024-05-04T15:04:49.220137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
26.8%
0.1 15
 
4.3%
0.9 9
 
2.6%
0.8 8
 
2.3%
0.3 7
 
2.0%
2.2 7
 
2.0%
0.4 7
 
2.0%
1.5 7
 
2.0%
1.6 5
 
1.4%
1.2 5
 
1.4%
Other values (114) 187
53.3%
ValueCountFrequency (%)
0 94
26.8%
0.1 15
 
4.3%
0.2 5
 
1.4%
0.3 7
 
2.0%
0.4 7
 
2.0%
0.5 5
 
1.4%
0.6 5
 
1.4%
0.7 1
 
0.3%
0.8 8
 
2.3%
0.9 9
 
2.6%
ValueCountFrequency (%)
102.6 1
0.3%
93.9 1
0.3%
86 1
0.3%
82.5 1
0.3%
62.9 1
0.3%
58.6 1
0.3%
53.5 1
0.3%
51.9 1
0.3%
51 1
0.3%
48.8 1
0.3%

ANNUAL
Real number (ℝ)

HIGH CORRELATION 

Distinct345
Distinct (%)98.6%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1009.7329
Minimum438
Maximum1747.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:49.405425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum438
5-th percentile681.98
Q1837.45
median975.9
Q31150.25
95-th percentile1432.305
Maximum1747.1
Range1309.1
Interquartile range (IQR)312.8

Descriptive statistics

Standard deviation234.43961
Coefficient of variation (CV)0.23217983
Kurtosis-0.07054091
Mean1009.7329
Median Absolute Deviation (MAD)151.15
Skewness0.50207288
Sum353406.5
Variance54961.929
MonotonicityNot monotonic
2024-05-04T15:04:49.610015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1118.9 2
 
0.6%
769 2
 
0.6%
1342.2 2
 
0.6%
1064.7 2
 
0.6%
1443.9 2
 
0.6%
718.2 1
 
0.3%
537.8 1
 
0.3%
603.5 1
 
0.3%
837.9 1
 
0.3%
784 1
 
0.3%
Other values (335) 335
95.4%
ValueCountFrequency (%)
438 1
0.3%
509.4 1
0.3%
519.9 1
0.3%
537.8 1
0.3%
544.3 1
0.3%
594.7 1
0.3%
600.3 1
0.3%
603.5 1
0.3%
644.1 1
0.3%
644.5 1
0.3%
ValueCountFrequency (%)
1747.1 1
0.3%
1726.8 1
0.3%
1639.3 1
0.3%
1598.8 1
0.3%
1533.7 1
0.3%
1521.9 1
0.3%
1518.7 1
0.3%
1515.8 1
0.3%
1508.4 1
0.3%
1504.2 1
0.3%

JF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct220
Distinct (%)62.9%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean19.229429
Minimum0
Maximum150.3
Zeros18
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:49.802504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.045
Q12.6
median9.45
Q327.625
95-th percentile67.24
Maximum150.3
Range150.3
Interquartile range (IQR)25.025

Descriptive statistics

Standard deviation24.292817
Coefficient of variation (CV)1.2633145
Kurtosis5.1481997
Mean19.229429
Median Absolute Deviation (MAD)9.05
Skewness2.0829515
Sum6730.3
Variance590.14094
MonotonicityNot monotonic
2024-05-04T15:04:49.990705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
5.1%
0.1 13
 
3.7%
0.2 10
 
2.8%
5.3 5
 
1.4%
1.7 5
 
1.4%
1.6 4
 
1.1%
5.5 4
 
1.1%
1.5 4
 
1.1%
3 4
 
1.1%
0.4 4
 
1.1%
Other values (210) 279
79.5%
ValueCountFrequency (%)
0 18
5.1%
0.1 13
3.7%
0.2 10
2.8%
0.3 3
 
0.9%
0.4 4
 
1.1%
0.5 3
 
0.9%
0.7 1
 
0.3%
0.8 1
 
0.3%
1 2
 
0.6%
1.1 1
 
0.3%
ValueCountFrequency (%)
150.3 1
0.3%
120.6 1
0.3%
117.4 1
0.3%
110.6 1
0.3%
110.1 1
0.3%
99.4 1
0.3%
97.9 1
0.3%
95.8 1
0.3%
93 1
0.3%
91.3 1
0.3%

MAM
Real number (ℝ)

HIGH CORRELATION 

Distinct273
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.840456
Minimum0
Maximum122.5
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:50.189952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median19.8
Q336.2
95-th percentile77.45
Maximum122.5
Range122.5
Interquartile range (IQR)27.2

Descriptive statistics

Standard deviation23.690063
Coefficient of variation (CV)0.88262522
Kurtosis2.2840618
Mean26.840456
Median Absolute Deviation (MAD)12
Skewness1.5220854
Sum9421
Variance561.2191
MonotonicityNot monotonic
2024-05-04T15:04:50.391718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.8 4
 
1.1%
4.2 3
 
0.9%
8.2 3
 
0.9%
5.3 3
 
0.9%
4 3
 
0.9%
11.3 3
 
0.9%
31.8 3
 
0.9%
12.4 3
 
0.9%
6.9 3
 
0.9%
8.9 3
 
0.9%
Other values (263) 320
91.2%
ValueCountFrequency (%)
0 1
0.3%
0.1 1
0.3%
0.6 1
0.3%
1.1 1
0.3%
1.3 1
0.3%
1.4 2
0.6%
1.6 1
0.3%
2 1
0.3%
2.1 1
0.3%
2.3 2
0.6%
ValueCountFrequency (%)
122.5 1
0.3%
121.2 1
0.3%
114.5 1
0.3%
105.5 1
0.3%
103.6 1
0.3%
99.7 1
0.3%
97.2 1
0.3%
95 1
0.3%
94 1
0.3%
88.4 1
0.3%

JJAS
Real number (ℝ)

HIGH CORRELATION 

Distinct346
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean893.11595
Minimum287.6
Maximum1477.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:50.604170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum287.6
5-th percentile586.6
Q1735
median863.4
Q31041.5
95-th percentile1293.1
Maximum1477.5
Range1189.9
Interquartile range (IQR)306.5

Descriptive statistics

Standard deviation220.15977
Coefficient of variation (CV)0.24650749
Kurtosis-0.30537802
Mean893.11595
Median Absolute Deviation (MAD)148.4
Skewness0.40537877
Sum313483.7
Variance48470.326
MonotonicityNot monotonic
2024-05-04T15:04:50.810632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039.6 2
 
0.6%
714.2 2
 
0.6%
803.1 2
 
0.6%
953 2
 
0.6%
737.7 2
 
0.6%
667.9 1
 
0.3%
745.1 1
 
0.3%
483.6 1
 
0.3%
665.9 1
 
0.3%
597.5 1
 
0.3%
Other values (336) 336
95.7%
ValueCountFrequency (%)
287.6 1
0.3%
449.5 1
0.3%
457.6 1
0.3%
472.8 1
0.3%
478.1 1
0.3%
483.6 1
0.3%
502.5 1
0.3%
514.5 1
0.3%
516.8 1
0.3%
539.6 1
0.3%
ValueCountFrequency (%)
1477.5 1
0.3%
1457.3 1
0.3%
1453.7 1
0.3%
1443.6 1
0.3%
1432.4 1
0.3%
1391.4 1
0.3%
1363.6 1
0.3%
1362.8 1
0.3%
1362.5 1
0.3%
1350.3 1
0.3%

OND
Real number (ℝ)

HIGH CORRELATION 

Distinct321
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.476068
Minimum0
Maximum276.3
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-04T15:04:51.002735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.25
Q125.65
median58.2
Q3103
95-th percentile177.1
Maximum276.3
Range276.3
Interquartile range (IQR)77.35

Descriptive statistics

Standard deviation55.053905
Coefficient of variation (CV)0.79241538
Kurtosis0.48078651
Mean69.476068
Median Absolute Deviation (MAD)36.2
Skewness0.97164353
Sum24386.1
Variance3030.9325
MonotonicityNot monotonic
2024-05-04T15:04:51.174002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 3
 
0.9%
7.9 2
 
0.6%
37.8 2
 
0.6%
126.4 2
 
0.6%
50.2 2
 
0.6%
87.9 2
 
0.6%
14.5 2
 
0.6%
85.6 2
 
0.6%
58.2 2
 
0.6%
73.5 2
 
0.6%
Other values (311) 330
94.0%
ValueCountFrequency (%)
0 1
0.3%
0.1 1
0.3%
0.2 1
0.3%
0.3 1
0.3%
0.9 2
0.6%
1.1 1
0.3%
1.3 1
0.3%
1.4 1
0.3%
1.8 1
0.3%
2.1 1
0.3%
ValueCountFrequency (%)
276.3 1
0.3%
240.6 1
0.3%
239.2 1
0.3%
237.7 1
0.3%
221.8 1
0.3%
206.7 1
0.3%
206 1
0.3%
203.8 1
0.3%
203 1
0.3%
202.6 1
0.3%

Interactions

2024-05-04T15:04:38.774766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:43.946381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:46.856049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:50.430713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:53.280216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:56.063705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:59.050865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:04.161432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:06.995386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:10.111302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:13.543246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:16.301607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:19.931217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:22.839627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:26.287339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:29.171260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:32.593461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:36.069450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:38.927827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:44.116754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:47.022417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-04T15:04:03.297489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:06.050768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:08.863389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:12.516758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:15.359183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:18.475740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:21.838257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:25.369892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:28.209339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:31.629911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:34.506700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:37.905413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:40.739054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:46.034271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:49.639299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:52.455612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:55.292483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:58.223862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:03.435525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:06.206179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:09.002228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:12.775621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:15.521976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:18.723167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:21.989394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:25.513903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:28.362211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:31.783294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:34.738708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:38.041033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:40.901830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:46.212059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:49.799221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:52.626470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:55.449764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:58.393365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:03.586112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:06.381898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:09.499985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:12.928409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:15.713684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:19.280690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:22.154765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:25.685063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:28.529651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:31.950820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:34.977042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:38.199118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:41.059880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:46.369874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:49.970776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:52.796424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:55.615919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:58.565204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:03.731434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:06.535602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:09.667018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:13.081814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:15.869601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:19.443586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:22.313262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:25.847223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:28.695351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:32.126023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:35.253216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:38.348924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:41.211988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:46.528734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:50.132226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:52.959000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:55.764673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:58.730943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:03.879588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:06.701262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:09.817563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:13.229059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:16.013142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:19.603442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:22.472827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:25.997561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:28.860936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:32.284728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:35.534136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:38.491679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:41.379884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:46.683824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:50.284999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:53.126411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:55.915214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:03:58.896902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:04.025072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:06.844090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:09.965114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:13.373060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:16.156158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:19.777406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:22.627331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:26.148167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:29.024174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:32.432828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:35.762350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-04T15:04:38.638356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-04T15:04:51.349087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ANNUALAPRAUGDECFEBJANJFJJASJULJUNMAMMARMAYNOVOCTONDSEPSUBDIVISIONYEAR
ANNUAL1.0000.0640.6580.0410.3400.2390.3800.9500.6280.2250.1020.188-0.066-0.0750.1830.1700.4840.4090.021
APR0.0641.000-0.1230.052-0.0590.011-0.042-0.0300.0520.1320.5210.1250.1940.0810.2240.208-0.0240.232-0.085
AUG0.658-0.1231.0000.0270.3560.3570.4470.7210.282-0.037-0.1580.184-0.308-0.145-0.126-0.1400.0970.4710.093
DEC0.0410.0520.0271.0000.192-0.0060.0720.0140.151-0.0810.0800.216-0.0920.197-0.1810.070-0.1010.032-0.117
FEB0.340-0.0590.3560.1921.0000.3380.7630.3560.299-0.039-0.0510.318-0.245-0.189-0.183-0.1880.0470.346-0.092
JAN0.2390.0110.357-0.0060.3381.0000.8020.2590.211-0.174-0.0590.194-0.217-0.195-0.159-0.1860.0370.276-0.103
JF0.380-0.0420.4470.0720.7630.8021.0000.3930.312-0.099-0.0780.270-0.277-0.247-0.177-0.2100.0600.399-0.137
JJAS0.950-0.0300.7210.0140.3560.2590.3931.0000.6580.193-0.0450.177-0.205-0.196-0.013-0.0710.4890.4430.042
JUL0.6280.0520.2820.1510.2990.2110.3120.6581.000-0.1060.0090.173-0.176-0.216-0.075-0.1160.1380.326-0.020
JUN0.2250.132-0.037-0.081-0.039-0.174-0.0990.193-0.1061.0000.196-0.0350.2290.0710.1330.172-0.0180.2150.046
MAM0.1020.521-0.1580.080-0.051-0.059-0.078-0.0450.0090.1961.0000.4430.6950.0800.2420.240-0.0400.275-0.170
MAR0.1880.1250.1840.2160.3180.1940.2700.1770.173-0.0350.4431.000-0.031-0.062-0.097-0.0900.0160.265-0.020
MAY-0.0660.194-0.308-0.092-0.245-0.217-0.277-0.205-0.1760.2290.695-0.0311.0000.2120.3280.335-0.0430.252-0.087
NOV-0.0750.081-0.1450.197-0.189-0.195-0.247-0.196-0.2160.0710.080-0.0620.2121.0000.1490.515-0.0550.202-0.085
OCT0.1830.224-0.126-0.181-0.183-0.159-0.177-0.013-0.0750.1330.242-0.0970.3280.1491.0000.8240.1490.3020.055
OND0.1700.208-0.1400.070-0.188-0.186-0.210-0.071-0.1160.1720.240-0.0900.3350.5150.8241.0000.0810.280-0.037
SEP0.484-0.0240.097-0.1010.0470.0370.0600.4890.138-0.018-0.0400.016-0.043-0.0550.1490.0811.0000.172-0.033
SUBDIVISION0.4090.2320.4710.0320.3460.2760.3990.4430.3260.2150.2750.2650.2520.2020.3020.2800.1721.0000.000
YEAR0.021-0.0850.093-0.117-0.092-0.103-0.1370.042-0.0200.046-0.170-0.020-0.087-0.0850.055-0.037-0.0330.0001.000

Missing values

2024-05-04T15:04:41.614929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T15:04:42.007538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-04T15:04:42.222862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SUBDIVISIONYEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJFMAMJJASOND
0West Madhya Pradesh190125.85.85.82.82.141.2228.9349.947.95.60.02.4718.231.610.7667.97.9
1West Madhya Pradesh190222.18.40.02.05.935.9401.9179.4194.137.910.014.2911.730.58.0811.262.0
2West Madhya Pradesh19035.30.00.00.022.350.6304.9261.1250.255.10.00.0949.65.322.3866.855.1
3West Madhya Pradesh19043.215.514.80.012.096.6273.0218.6125.93.31.89.6774.418.726.9714.114.7
4West Madhya Pradesh19053.54.41.10.83.036.1326.3137.6183.50.30.00.0696.57.94.9683.50.3
5West Madhya Pradesh19060.011.06.80.00.5180.0344.5198.6266.21.50.20.91010.211.07.3989.32.6
6West Madhya Pradesh19075.225.10.612.32.848.7202.2328.517.30.07.80.0650.430.315.7596.67.8
7West Madhya Pradesh190812.10.610.11.51.9104.5368.8281.948.50.50.41.8832.612.713.6803.62.7
8West Madhya Pradesh19093.52.10.424.89.8150.0246.2257.9106.80.90.027.9830.25.635.0760.828.8
9West Madhya Pradesh19101.50.00.30.30.7171.0183.5273.9244.150.333.80.0959.51.51.3872.684.1
SUBDIVISIONYEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJFMAMJJASOND
341Madhya Maharashtra20080.00.018.10.61.1107.9157.9257.6312.456.63.70.9916.90.019.9835.961.1
342Madhya Maharashtra20090.01.70.80.47.768.9329.8148.2162.696.2121.34.3941.71.78.9709.4221.8
343Madhya Maharashtra20102.90.10.92.35.4185.6280.9233.2165.677.173.40.21027.53.08.6865.3150.6
344Madhya Maharashtra20110.00.30.35.02.9133.3261.4238.1148.462.80.00.0852.60.38.2781.362.8
345Madhya Maharashtra20120.00.00.03.01.467.9203.0187.8129.595.22.20.0689.80.04.4588.197.3
346Madhya Maharashtra20130.15.30.85.76.0212.4311.8147.0210.357.84.01.3962.45.312.4881.563.1
347Madhya Maharashtra20143.16.224.47.529.844.0277.9240.3120.438.532.813.1838.09.361.7682.684.4
348Madhya Maharashtra20151.40.841.29.624.4177.0111.767.2146.648.316.20.1644.52.275.3502.564.5
349Madhya Maharashtra20160.00.77.11.47.7100.7319.6232.7171.962.41.10.9906.30.716.2824.964.4
350Madhya Maharashtra20170.00.00.40.818.0189.2280.9192.0190.1112.85.05.1994.40.019.2852.3122.9